Big Data

In the information age, the challenge is no longer collecting data but instead making sense of it. These are just a few of the ways that researchers in the Department of Computer Science are making use of "big data" in their work.

Digging into Archaeological Data

Archaeologists are known for sifting through sand and dirt looking for buried treasure. But the modern archaeologist spends just as much time sifting through thousands of digital images looking for the data they need to answer important historical questions. Dr. Mark Eramian wants to build a new online tool to ease that process by using automated image-recognition techniques. Right now, if an archaeologist searches for "Roman vases," existing tools can only find images that have been manually labelled by humans as a "Roman vase." Dr. Eramian and his international, interdisciplinary team are developing computer methods that can recognize the images themselves based on shape, colour, texture and their similarities to other images, resulting in a richer, more accurate search tool for arcaheological researchers.

3D Computer Models of Medical Patients

CAT scans, MRIs and more - modern medical measurement techniques can produce an enormous amount of data even for one individual patient. Dr. Ian Stavness and his research lab build tools that can incorporate this data into meaningful 3D computer models of the patient. Doctors can then use these models to plan medical interventions, predict their efficacy, and communicate information to the patients themselves. Dr. Stavness' work is in collaboration with the Parametric Human Project - an academic and industrial research consortium that studies all aspects of digital human modeling.

Computer Modeling for Public Health

Computer models of large-scale human populations can be powerful tools for predicting the spread of infectious disease and exploring a myriad of issues relevant to public health. But computer models are only as good as the data used to build them. To this end, Dr. Nate Osgood and Dr. Kevin Stanley and their teams developed their "iEpi" data-collection software. The software is meant to run on smartphones, which volunteers carry with them during their day-to-day routine. Not only does the software track the volunteer's daily movements using GPS, but it can also detect, via wireless sensing, whenever two smartphones running iEpi come into close proximity. The software can also routinely query study participants with short, context-sensitive questions about their daily activities. The software has been employed in a variety of studies at universities throughout Canada and the United States in order to study a diverse range of public health issues.

Predicting and Adapting in Educational Software

When users interact with a digital system, they generate a rich history of interaction events. When these events are analyzed at the level of individual mouse-clicks, the amount of data generated is enormous. Dr. Gord McCalla and his team have used this data to characterize user's interactions with educational software. Based on a user's clickstream, Dr. McCalla has developed methods that accurately predict how well the user is learning the educational material. This in turn allows the software to adapt to the individual user's needs and level of expertise, paving the way for truly adaptive large-scale online learning environments.